OpenRaiser/Intern-Atlas huggingface dataset:
RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Personalized image generation is crucial for improving the user experience, as it renders reference images into preferred ones according to user visual preferences. Although effective, existing methods face two main issues. First, existing methods treat all items in the user historical sequence equally when extracting user preferences, overlooking the varying semantic similarities between historical items and the reference item. Disproportionately high weights for low-similarity items distort users' visual preferences for the reference item. Second, existing methods heavily rely on consistency between generated and reference images to optimize the generation, which leads to underfitting user preferences and hinders personalization. To address these issues, we propose Retrieval Augment Personalized Image GenerAtion guided by Recommendation (RAGAR). Our approach uses a retrieval mechanism to assign different weights to historical items according to their similarities to the reference item, thereby extracting more refined users' visual preferences for the reference item. Then we introduce a novel rank task based on the multi-modal ranking model to optimize the personalization of the generated images instead of forcing depend on consistency. Extensive experiments and human evaluations on three real-world datasets demonstrate that RAGAR achieves significant improvements in both personalization and semantic metrics compared to five baselines.
Sensing Safety Analysis for Vehicular Networks with Integrated Sensing and Communication (ISAC)
Venue: arXiv cs.IT 2025
Year: 2025

Integrated sensing and communication (ISAC) emerged as a key feature of next-generation 6G wireless systems, allowing them to achieve high data rates and sensing accuracy. While prior research has primarily focused on addressing communication safety in ISAC systems, the equally critical issue of sensing safety remains largely ignored. In this paper, a novel threat to the sensing safety of ISAC vehicle networks is studied, whereby a malicious reconfigurable intelligent surface (RIS) is deployed to compromise the sensing functionality of a roadside unit (RSU). Specifically, a malicious attacker dynamically adjusts the phase shifts of an RIS to spoof the sensing outcomes of a vehicular user (VU)'s echo delay, Doppler shift, and angle-of-departure (AoD). To achieve spoofing on Doppler shift estimation, a time-varying phase shift design on the RIS is proposed. Furthermore, the feasible spoofing frequency set with respect to the Doppler shift is analytical derived. Analytical results also demonstrate that the maximum likelihood estimator (MLE) of the AoD can be significantly misled under spoofed Doppler shift estimation. Simulation results validate our theoretical findings, showing that the RIS can induce a spoofed velocity estimation from 0.1 m/s to 14.9 m/s for a VU with velocity of 10 m/s, and can cause an AoD estimation error of up to 65^{\circ} with only a 5^{\circ} beam misalignment.
An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding
Venue: arXiv cs.CV 2025
Year: 2025

The rapid advancements in Large Vision Language Models (LVLMs) offer the potential to surpass conventional labeling by generating richer, more detailed descriptions of on-device human behavior understanding (HBU) in low-resolution vision systems, such as depth, thermal, and infrared. However, existing large vision language model (LVLM) approaches are unable to understand low-resolution data well as they are primarily designed for high-resolution data, such as RGB images. A quick fixing approach is to caption a large amount of low-resolution data, but it requires a significant amount of labor-intensive annotation efforts. In this paper, we propose a novel, labor-saving system, Llambda, designed to support low-resolution HBU. The core idea is to leverage limited labeled data and a large amount of unlabeled data to guide LLMs in generating informative captions, which can be combined with raw data to effectively fine-tune LVLM models for understanding low-resolution videos in HBU. First, we propose a Contrastive-Oriented Data Labeler, which can capture behavior-relevant information from long, low-resolution videos and generate high-quality pseudo labels for unlabeled data via contrastive learning. Second, we propose a Physical-Knowledge Guided Captioner, which utilizes spatial and temporal consistency checks to mitigate errors in pseudo labels. Therefore, it can improve LLMs' understanding of sequential data and then generate high-quality video captions. Finally, to ensure on-device deployability, we employ LoRA-based efficient fine-tuning to adapt LVLMs for low-resolution data. We evaluate Llambda using a region-scale real-world testbed and three distinct low-resolution datasets, and the experiments show that Llambda outperforms several state-of-the-art LVLM systems up to $40.03\%$ on average Bert-Score.
Memory-Efficient LLM Training by Various-Grained Low-Rank Projection of Gradients
Venue: arXiv cs.LG 2025
Year: 2025

Building upon the success of low-rank adapter (LoRA), low-rank gradient projection (LoRP) has emerged as a promising solution for memory-efficient fine-tuning. However, existing LoRP methods typically treat each row of the gradient matrix as the default projection unit, leaving the role of projection granularity underexplored. In this work, we propose a novel framework, VLoRP, that extends low-rank gradient projection by introducing an additional degree of freedom for controlling the trade-off between memory efficiency and performance, beyond the rank hyper-parameter. Through this framework, we systematically explore the impact of projection granularity, demonstrating that finer-grained projections lead to enhanced stability and efficiency even under a fixed memory budget. Regarding the optimization for VLoRP, we present ProjFactor, an adaptive memory-efficient optimizer, that significantly reduces memory requirement while ensuring competitive performance, even in the presence of gradient accumulation. Additionally, we provide a theoretical analysis of VLoRP, demonstrating the descent and convergence of its optimization trajectory under both SGD and ProjFactor. Extensive experiments are conducted to validate our findings, covering tasks such as commonsense reasoning, MMLU, and GSM8K.
Unified Steganography via Implicit Neural Representation
Venue: arXiv cs.CR 2025
Year: 2025

Digital steganography is the practice of concealing for encrypted data transmission. Typically, steganography methods embed secret data into cover data to create stega data that incorporates hidden secret data. However, steganography techniques often require designing specific frameworks for each data type, which restricts their generalizability. In this paper, we present U-INR, a novel method for steganography via Implicit Neural Representation (INR). Rather than using the specific framework for each data format, we directly use the neurons of the INR network to represent the secret data and cover data across different data types. To achieve this idea, a private key is shared between the data sender and receivers. Such a private key can be used to determine the position of secret data in INR networks. To effectively leverage this key, we further introduce a key-based selection strategy that can be used to determine the position within the INRs for data storage. Comprehensive experiments across multiple data types, including images, videos, audio, and SDF and NeRF, demonstrate the generalizability and effectiveness of U-INR, emphasizing its potential for improving data security and privacy in various applications.
From Formulas to Figures: How Visual Elements Impact User Interactions in Educational Videos
Venue: arXiv cs.HC 2025
Year: 2025

Educational videos have become increasingly relevant in today's learning environments. While prior research in laboratory studies has provided valuable insights, analyzing real-world interaction data can enhance our understanding of authentic user behavior. Previous studies have investigated technical aspects, such as the influence of cuts on pausing behavior, but the impact of visual complexity remains understudied. In this paper, we address this gap and propose a novel approach centered on visual complexity, defined as the number of visually distinguishable and meaningful elements in a video frame, such as mathematical equations, chemical formulas, or graphical representations. Our study introduces a fine-grained taxonomy of visual objects in educational videos, expanding on previous classifications. Applying this taxonomy to 25 videos from physics and chemistry, we examine the relationship between visual complexity and user behavior, including pauses, in-video navigation, and session dropouts. The results indicate that increased visual complexity, especially of textual elements, correlates with more frequent pauses, rewinds, and dropouts. The results offer a deeper understanding of how video design affects user behavior in real-world scenarios. Our work has implications for optimizing educational videos, particularly in STEM fields. We make our code publicly available (https://github.com/TIBHannover/from_formulas_to_figures).
Efficient 3D Full-Body Motion Generation from Sparse Tracking Inputs with Temporal Windows
Venue: arXiv cs.CV 2025
Year: 2025

To have a seamless user experience on immersive AR/VR applications, the importance of efficient and effective Neural Network (NN) models is undeniable, since missing body parts that cannot be captured by limited sensors should be generated using these models for a complete 3D full-body reconstruction in virtual environment. However, the state-of-the-art NN-models are typically computational expensive and they leverage longer sequences of sparse tracking inputs to generate full-body movements by capturing temporal context. Inevitably, longer sequences increase the computation overhead and introduce noise in longer temporal dependencies that adversely affect the generation performance. In this paper, we propose a novel Multi-Layer Perceptron (MLP)-based method that enhances the overall performance while balancing the computational cost and memory overhead for efficient 3D full-body generation. Precisely, we introduce a NN-mechanism that divides the longer sequence of inputs into smaller temporal windows. Later, the current motion is merged with the information from these windows through latent representations to utilize the past context for the generation. Our experiments demonstrate that generation accuracy of our method with this NN-mechanism is significantly improved compared to the state-of-the-art methods while greatly reducing computational costs and memory overhead, making our method suitable for resource-constrained devices.
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
Venue: arXiv cs.DC 2025
Year: 2025

Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems, yet introduce significant challenges in model deployment and resource management. In this survey, we comprehensive examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, enabling technologies, and emerging applications. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and latency requirements. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examines practical deployments through diverse applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements and practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments, fostering innovation in next-generation intelligent systems.
PhytoSynth: Leveraging Multi-modal Generative Models for Crop Disease Data Generation with Novel Benchmarking and Prompt Engineering Approach
Venue: arXiv cs.CV 2025
Year: 2025

Collecting large-scale crop disease images in the field is labor-intensive and time-consuming. Generative models (GMs) offer an alternative by creating synthetic samples that resemble real-world images. However, existing research primarily relies on Generative Adversarial Networks (GANs)-based image-to-image translation and lack a comprehensive analysis of computational requirements in agriculture. Therefore, this research explores a multi-modal text-to-image approach for generating synthetic crop disease images and is the first to provide computational benchmarking in this context. We trained three Stable Diffusion (SD) variants-SDXL, SD3.5M (medium), and SD3.5L (large)-and fine-tuned them using Dreambooth and Low-Rank Adaptation (LoRA) fine-tuning techniques to enhance generalization. SD3.5M outperformed the others, with an average memory usage of 18 GB, power consumption of 180 W, and total energy use of 1.02 kWh/500 images (0.002 kWh per image) during inference task. Our results demonstrate SD3.5M's ability to generate 500 synthetic images from just 36 in-field samples in 1.5 hours. We recommend SD3.5M for efficient crop disease data generation.
ResiTok: A Resilient Tokenization-Enabled Framework for Ultra-Low-Rate and Robust Image Transmission
Venue: arXiv cs.IT 2025
Year: 2025

Real-time transmission of visual data over wireless networks remains highly challenging, even when leveraging advanced deep neural networks, particularly under severe channel conditions such as limited bandwidth and weak connectivity. In this paper, we propose a novel Resilient Tokenization-Enabled (ResiTok) framework designed for ultra-low-rate image transmission that achieves exceptional robustness while maintaining high reconstruction quality. By reorganizing visual information into hierarchical token groups consisting of essential key tokens and supplementary detail tokens, ResiTok enables progressive encoding and graceful degradation of visual quality under constrained channel conditions. A key contribution is our resilient 1D tokenization method integrated with a specialized zero-out training strategy, which systematically simulates token loss during training, empowering the neural network to effectively compress and reconstruct images from incomplete token sets. Furthermore, the channel-adaptive coding and modulation design dynamically allocates coding resources according to prevailing channel conditions, yielding superior semantic fidelity and structural consistency even at extremely low channel bandwidth ratios. Evaluation results demonstrate that ResiTok outperforms state-of-the-art methods in both semantic similarity and visual quality, with significant advantages under challenging channel conditions.
A computational framework for predicting the effect of surface roughness in fatigue
Venue: arXiv cs.CE 2025
Year: 2025

Surface roughness is a critical factor influencing the fatigue life of structural components. Its effect is commonly quantified using a correction coefficient known as the surface factor. In this paper, a phase field based numerical framework is proposed to estimate the surface factor while accounting for the stochastic nature of surface roughness. The model is validated against existing experimental data. Furthermore, we investigate the influence of key parameters on the fatigue life of rough surfaces, such as surface topology and failure strength. An important effect of surface roughness is observed when the average surface roughness increases and the correlation length of the surface profile decreases. This effect becomes more pronounced with higher failure strengths.
PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications
Venue: arXiv cs.CV 2025
Year: 2025

Robust navigation in diverse environments and domains requires both accurate state estimation and transparent decision making. We present PhysNav-DG, a novel framework that integrates classical sensor fusion with the semantic power of vision-language models. Our dual-branch architecture predicts navigation actions from multi-sensor inputs while simultaneously generating detailed chain-of-thought explanations. A modified Adaptive Kalman Filter dynamically adjusts its noise parameters based on environmental context. It leverages several streams of raw sensor data along with semantic insights from models such as LLaMA 3.2 11B and BLIP-2. To evaluate our approach, we introduce the MD-NEX Benchmark, a novel multi-domain dataset that unifies indoor navigation, autonomous driving, and social navigation tasks with ground-truth actions and human-validated explanations. Extensive experiments and ablations show that PhysNav-DG improves navigation success rates by over 20% and achieves high efficiency, with explanations that are both highly grounded and clear. This work connects high-level semantic reasoning and geometric planning for safer and more trustworthy autonomous systems.
From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of FIFA World Cup match outcomes holds significant value for analysts, coaches, bettors, and fans. This paper presents a machine learning framework specifically designed to forecast match winners in FIFA World Cup. By integrating both team-level historical data and player-specific performance metrics such as goals, assists, passing accuracy, and tackles, we capture nuanced interactions often overlooked by traditional aggregate models. Our methodology processes multi-year data to create year-specific team profiles that account for evolving rosters and player development. We employ classification techniques complemented by dimensionality reduction and hyperparameter optimization, to yield robust predictive models. Experimental results on data from the FIFA 2022 World Cup demonstrate our approach's superior accuracy compared to baseline method. Our findings highlight the importance of incorporating individual player attributes and team-level composition to enhance predictive performance, offering new insights into player synergy, strategic match-ups, and tournament progression scenarios. This work underscores the transformative potential of rich, player-centric data in sports analytics, setting a foundation for future exploration of advanced learning architectures such as graph neural networks to model complex team interactions.
Continuously Ordered Hierarchies of Algorithmic Information in Digital Twinning and Signal Processing
Venue: arXiv cs.IT 2025
Year: 2025

We consider a fractional-calculus example of a continuous hierarchy of algorithmic information in the context of its potential applications in digital twinning. Digital twinning refers to different emerging methodologies in control engineering that involve the creation of a digital replica of some physical entity. From the perspective of computability theory, the problem of ensuring the digital twin's integrity -- i.e., keeping it in a state where it matches its physical counterpart -- entails a notion of algorithmic information that determines which of the physical system's properties we can reliably deduce by algorithmically analyzing its digital twin. The present work investigates the fractional calculus of periodic functions -- particularly, we consider the Wiener algebra -- as an exemplary application of the algorithmic-information concept. We establish a continuously ordered hierarchy of algorithmic information among spaces of periodic functions -- depending on their fractional degree of smoothness -- in which the ordering relation determines whether a certain representation of some function contains ``more'' or ``less'' information than another. Additionally, we establish an analogous hierarchy among lp-spaces, which form a cornerstone of (traditional) digital signal processing. Notably, both hierarchies are (mathematically) ``dual'' to each other. From a practical perspective, our approach ultimately falls into the category of formal verification and (general) formal methods.
Explainability by design: an experimental analysis of the legal coding process
Venue: arXiv cs.LO 2025
Year: 2025

Behind a set of rules in Deontic Defeasible Logic, there is a mapping process of normative background fragments. This process goes from text to rules and implicitly encompasses an explanation of the coded fragments. In this paper we deliver a methodology for \textit{legal coding} that starts with a fragment and goes onto a set of Deontic Defeasible Logic rules, involving a set of \textit{scenarios} to test the correctness of the coded fragments. The methodology is illustrated by the coding process of an example text. We then show the results of a series of experiments conducted with humans encoding a variety of normative backgrounds and corresponding cases in which we have measured the efforts made in the coding process, as related to some measurable features. To process these examples, a recently developed technology, Houdini, that allows reasoning in Deontic Defeasible Logic, has been employed. Finally we provide a technique to forecast time required in coding, that depends on factors such as knowledge of the legal domain, knowledge of the coding processes, length of the text, and a measure of \textit{depth} that refers to the length of the paths of legal references.
Generative AI in clinical practice: novel qualitative evidence of risk and responsible use of Google's NotebookLM
Venue: arXiv cs.AI 2025
Year: 2025

The advent of generative artificial intelligence, especially large language models (LLMs), presents opportunities for innovation in research, clinical practice, and education. Recently, Dihan et al. lauded LLM tool NotebookLM's potential, including for generating AI-voiced podcasts to educate patients about treatment and rehabilitation, and for quickly synthesizing medical literature for professionals. We argue that NotebookLM presently poses clinical and technological risks that should be tested and considered prior to its implementation in clinical practice.